Instructions to use Xenova/tiny-random-LlavaForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xenova/tiny-random-LlavaForConditionalGeneration with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('image-text-to-text', 'Xenova/tiny-random-LlavaForConditionalGeneration');

Transformers

How to use Xenova/tiny-random-LlavaForConditionalGeneration with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Xenova/tiny-random-LlavaForConditionalGeneration")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Xenova/tiny-random-LlavaForConditionalGeneration")
model = AutoModelForMultimodalLM.from_pretrained("Xenova/tiny-random-LlavaForConditionalGeneration", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Xenova/tiny-random-LlavaForConditionalGeneration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xenova/tiny-random-LlavaForConditionalGeneration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenova/tiny-random-LlavaForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Xenova/tiny-random-LlavaForConditionalGeneration

SGLang

How to use Xenova/tiny-random-LlavaForConditionalGeneration with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Xenova/tiny-random-LlavaForConditionalGeneration" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenova/tiny-random-LlavaForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Xenova/tiny-random-LlavaForConditionalGeneration" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xenova/tiny-random-LlavaForConditionalGeneration",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Xenova/tiny-random-LlavaForConditionalGeneration with Docker Model Runner:
```
docker model run hf.co/Xenova/tiny-random-LlavaForConditionalGeneration
```

tiny-random-LlavaForConditionalGeneration / onnx /decoder_model_uint8.onnx

Xenova HF Staff

Upload folder using huggingface_hub

8f296ac verified over 2 years ago

History Blame Contribute Delete

4.84 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 679d5fac92c81e32685dc660968e7144ff2b4fc065fd5b37ff7dc639d079b1aa
Size of remote file:: 4.84 MB
SHA256:: c1151c45098f666710d012b7d890952510e804815129561751611ef2b43ee6da

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.